Duvo MCP

Consume Agent Suggestion

consumeAgentSuggestion

Apply a suggestion: stage its change into the Agent's draft revision, then auto-clear any pending suggestions it makes moot.

Input Schema

TableJSON Schema

Name	Required	Description
`id`	Yes	The suggestion's unique identifier
`revision_id`	No
`create_new_draft`	No
`selected_automation_revision_id`	No	The automation revision this edit is being made at. Pass a draft and the edit is folded into that draft itself; pass the active revision and the edit lands on a draft branched from it. A historic revision is rejected — it cannot be activated from without an explicit rebase. A revision belonging to a different automation is ignored, and the edit targets the automation the addressed agent belongs to. Omit to target the automation's active revision.

TDQS

A3.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations only mark the operation as non-read-only, non-idempotent, and non-destructive. The description adds meaningful behavioral context: it stages the change into the draft revision and automatically clears other suggestions that become moot. This goes beyond the annotation flags and informs the agent of side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly-worded sentence that front-loads the action ('Apply a suggestion') and includes necessary behavioral details without any filler. Every word contributes to understanding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description conveys the overall workflow (stage change, clear moot suggestions) but leaves gaps about optional parameter semantics and edge cases (e.g., what happens if no draft exists, what 'moot' means exactly). For a mutation tool with four parameters and no output schema, this is adequate but not fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not mention any parameters or tie them to behavior. Schema description coverage is only 50% (id and selected_automation_revision_id have descriptions, while revision_id and create_new_draft lack any). The description fails to compensate for the undocumented parameters, leaving the agent without enough information to set them correctly.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'Apply' and identifies the resource 'suggestion', then details the core action: staging the change into the Agent's draft revision and auto-clearing moot pending suggestions. This clearly distinguishes it from sibling tools like rejectAgentSuggestion or getAgentSuggestion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for accepting/consuming a suggestion, but it does not explicitly state when to use it versus alternatives or provide any exclusions. Sibling tool names hint at alternatives (e.g., rejectAgentSuggestion) but the description itself offers no guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

B3.1/5.0

Disambiguation2/5

Despite detailed descriptions, many tool names are highly ambiguous, with multiple tools covering the same conceptual actions (e.g., acceptClarityCaptureSuggestion vs. acceptClarityTeamAssignmentSuggestion, or the many deleteClarity*Interview tools). The set is so large that distinguishing between, say, listClarityFolders, listClarityProcesses, and listClarityProcessSummaries requires reading deep into descriptions, reducing agent selection accuracy.

Naming Consistency4/5

The naming convention is predominantly verb_noun (e.g., createClarityProcess, listAgents, deleteQueue), and is remarkably consistent across the 316 tools. There are only minor deviations, such as 'fileSuggestedClarityProcesses' (verb + adjective noun) and 'bulkUpdateCasePriority' (where 'bulk' could be seen as a prefix), but overall the pattern holds strongly.

Tool Count1/5

With 316 tools, this server is extremely oversized for any single agent to manage effectively. The massive number of tools suggests poor modularization—many of these tools likely belong in separate, smaller servers focused on specific domains (e.g., Clarity, Pulse, Agent management). The cognitive load for an agent to choose from 316 options is very high, leading to frequent misselection.

Completeness4/5

The tool surface covers an extraordinarily wide range of operations across the Duvo platform: agents, runs, cases, queues, Clarity processes, skills, integrations, notifications, teams, and more. Most resource types have full CRUD and lifecycle management. Notable minor gaps exist (e.g., no tools for managing specific notification batch severities dynamically, and some interview management is missing batch operations), but for the platform's scope, coverage is impressively thorough.

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